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Hernan and ihlsC Lammers C. Nicholas development embryonic in formation pattern of control transcriptional Multimodal www.pnas.org/cgi/doi/10.1073/pnas.1912500117 ipyisGaut ru,Uiest fClfri,Bree,C 94720; CA Berkeley, California, of University Group, Graduate Biophysics rdcigdvlpetlotoe ead quantitative a demands outcomes developmental Predicting Drosophila en fgn xrsinsalwgains hr steps, sharp gradients, expression—shallow gene pat- of choreographed tightly terns development, embryonic uring mro sacs td,w isc h regula- the dissect we study, case a as embryos e eateto ple hsc n ple ahmtc,Clmi nvriy e ok Y10027; NY York, New University, Columbia Mathematics, Applied and Physics Applied of Department i eateto oeua n elBooy nvriyo aiona ekly A970 and 94720; CA Berkeley, California, of University Biology, Cell and Molecular of Department | eeregulation gene a,1 a,d,i,j,2 even-skipped aeGalstyan Vahe , g eateto ytm ilg,Clmi nvriy e ok Y10027; NY York, New University, Columbia Biology, Systems of Department c eateto hsc,Clmi nvriy e ok Y10027; NY York, New University, Columbia Physics, of Department | development ly rtclrl nstripe in role critical a plays b,c,1 even-skipped rad Reimer Armando , | gene b iceityadMlclrBohsc pin aionaIsiueof Institute California Option, Biophysics Molecular and Biochemistry a ftemlclrpoessa ly ti eesr omeasure to necessary is it play, at processes molecular understanding the deeper a of toward move thereby and formation tern combina- mRNA some cytoplasmic a or of scenarios, formation the pattern. these explain than of can center thereof, stripe Any tion the boundaries. in the production fraction mRNA in larger in a engage Here, 1D). nuclei dur- (Fig. all of pattern at the transcription of formation in the engage ing time. not of might lengths nuclei different regardless for some but rate Finally, stripe, average the along same position dic- their the of that at quiescent— or transcribe switch active nuclei on/off transcriptionally individual In is an nucleus 1C). a to (Fig. whether scheme—akin active tates expression. transcriptionally control is binary gene nucleus the this a of over time control control of exert transcriptional length could analog factors graded the transcription this Alternatively, with identify strategy We stripe control 1B). the on nuclei (Fig. than rate boundaries average higher a at transcribe stripe doi:10.1073/pnas.1912500117/-/DCSupplemental. at online information supporting contains article This project’s the in found 2 be can work 1 this in used sources at data repository GitHub All deposition: Data control BY-NC-ND) (CC 4.0 NoDerivatives License distributed under is article access open This Submission.y Direct PNAS a is article This interest.y competing wrote no H.G.G. declare and authors V.G., The N.C.L., and data; analyzed paper. H.G.G. y the and reagents/analytic S.A.M., new V.G., contributed per- C.H.W. N.C.L., and H.G.G. tools; S.A.M., and A.R., V.G., V.G., N.C.L., N.C.L., research; research; designed formed H.G.G. and C.H.W. contributions: Author owo orsodnemyb drse.Eal gacabree.d rchris. or [email protected] Email: addressed. be [email protected] may correspondence whom To work.y this to equally contributed V.G. and N.C.L. enA Medin A. 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DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY A ingly, however, we discovered that this analog control of the transcription rate is insufficient to quantitatively recapitulate the cytoplasmic mRNA stripe pattern. In addition to the control of the rate of mRNA production among nuclei, we also observed a pronounced regulation of the window of time during which eve nuclei loci were engaged in transcription across the stripe, with those cytoplasmic in the stripe center expressing for approximately 3 times longer mRNA than those in the flanks. While it is widely appreciated that genes B are transcriptionally competent for limited windows of time dur- analog control ing development, we found that—in the case of eve stripe 2—this of the mean binary transcriptionally engaged/disengaged logic is not merely a transcription rate necessary precondition for pattern formation—it is the primary driver thereof. Thus, we conclude that the regulation of eve stripe active quiescent 2 is multimodal in nature, with contributions from 3 distinct regu- C RNAP latory strategies (Fig. 1 B–D). Nonetheless, stripe formation can loading rate binary control be quantitatively explained almost entirely through the interplay of the transcriptional between 2 distinct control strategies: binary control of the dura- time window tion of transcriptional engagement (Fig. 1C) and control of the D time mean rate of transcription (Fig. 1B). control of the Building upon this result, we developed computational approaches to uncover the mechanistic underpinning of each fraction of regulatory strategy. We employed a compound-state hidden active nuclei Markov model (cpHMM) to uncover variations in transcriptional Fig. 1. Multiple modes of pattern formation by single-cell transcriptional bursting dynamics in individual nuclei across space and time activity. (A–D) Cytoplasmic mRNA patterns (A) could arise from transcription (22–24). We uncovered that, consistent with previous results, factors exerting control over the mean transcription rate (B), the transcrip- transcription factors control the rate of transcription by alter- tional time window dictating when a nucleus is transcriptionally active or ing the frequency of transcriptional bursts (25, 26). In addition, quiescent (C), or the fraction of active nuclei (D) or some combination we utilized logistic regressions to correlate eve stripe 2 transcrip- thereof. tional dynamics with changes in input transcription factor con- centrations. This analysis revealed that the transcriptional time window adheres to different regulatory logic than transcriptional the rate of RNAP loading in individual nuclei, in real time, bursting: While repressor levels alone were sufficient to explain in a living embryo. However, to date, most studies have relied the early silencing of nuclei in the anterior and posterior stripe on fixed-tissue techniques such as mRNA fluorescence in situ flanks, the control of bursting among transcriptionally engaged hybridization (FISH) and immunofluorescence to obtain snap- nuclei depends upon the input concentrations of both activa- shots of the cytoplasmic distributions of mRNA and protein as tors and repressors. Thus, our findings point to the presence of development progresses (9, 12–15). Such techniques are virtu- 2 distinct regulatory mechanisms that control transcription and ally silent regarding the regulation of single-cell gene expression gene expression patterns in early development, showcasing the over time and are thus ill-suited to the study of how spatiotem- potential for theoretical modeling and biological numeracy to poral variations in transcriptional dynamics give rise to patterns yield additional biological insights when coupled with precise and of cytoplasmic mRNA. quantitative experimental observation. In this work, we investigated how single-cell transcriptional activity leads to the formation of stripe 2 of the widely studied Results even-skipped (eve) gene in the developing fruit fly embryo (16, Predicting Cytoplasmic mRNA Distributions from Transcriptional 17). Previous work has established that the stripe is formed Activity. To predict how the transcriptional activity of individual through the interplay of transcriptional activators and repressors nuclei dictates the formation of cytoplasmic patterns of mRNA, (16). In addition, recent studies have indicated that the eve stripe we began with a simple model that considers the balance between mRNA profiles are graded and highly reproducible (18–21), sug- the rate of mRNA synthesis and degradation gesting that the detailed cytoplasmic distribution of mRNA that makes these stripes is key to the transmission of spatial infor- dmRNA mation along the gene regulatory network that drives Drosophila (x, t) = pactive(x) R(x, t) − γ mRNA(x, t), [1] dt | {z } | {z } | {z } development and reinforcing the need to develop models of fraction of synthesis degradation gene regulation capable of connecting quantitative variations in active nuclei input transcription factor patterns to graded output rates of tran- scription. To do this, we combined live imaging with theoretical where mRNA(x, t) indicates the mRNA concentration at posi- modeling to study transcription at the single-cell level in real tion x along the embryo at time t, R(x, t) corresponds to the time, seeking a quantitative connection between the spatiotem- mRNA synthesis rate averaged over multiple nuclei within the poral variations in input transcription factor concentrations, the same position x, pactive(x) is the fraction of active nuclei (cor- control of eve transcription, and the formation of cytoplasmic responding to the regulatory strategy shown in Fig. 1D), and γ patterns of mRNA. is the degradation rate (see SI Appendix, section A for details of We found that all 3 regulatory strategies outlined in Fig. 1 this derivation). quantitatively contribute to the formation of eve stripe 2. First, a To examine the quantitative consequences of the 3 poten- smaller fraction of nuclei become active and engage in transcrip- tial regulatory strategies (Fig. 1 B–D), we adopted widespread tion in the periphery of the stripe than in the center, although assumptions in the modeling of transcriptional regulation. First, this regulation of the fraction of active nuclei makes only a we assumed that the degradation rate γ is a constant and minor contribution to stripe formation. Second, consistent with not under any kind of spatiotemporal control. Comparisons previous studies, we found that the rate of mRNA production between model predictions and empirically measured levels of is significantly elevated in the center of the stripe (18). Strik- cytoplasmic mRNA suggest that this assumption is reasonable

2 of 12 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on December 28, 2019 Downloaded by guest on December 28, 2019 eut h iewno vrwihec rncitoa locus transcriptional each which transcription, in over As engaged window S4C). is time Fig. Appendix , the (SI result, axis a nuclei embryo’s which the at along time modulation quiescent, the become S4 B), and transcribing Fig. stop , Appendix (SI active anaphase nucleus becoming ous each nuclei which all with at stripe, (SI time the stripe active, the transcriptionally the that Whereas becomes along S4A). revealed modulated Fig. data is Appendix, Our window time window. transcriptional time transcriptional the per molecules 8 boundaries. about of the to at rate decreases minute rate a loading at this loaded minute, per are molecules RNAP stripe and S3; Fig. Eq. quiescence transcriptional of time state a at time enter nuclei at and transcribing that start transcribing assumed embryo stop the we of Finally, of axis transcription. the regulation along time-dependent of the rate for mean account the to text the in later amr tal. et Lammers 3 along (Fig. modulated embryo is the rate of this axis that the found We estimate B. to section traces Appendix, loading, RNAP MS2 of rate our time-averaged Eq. of the pattern in intensities to model fluorescence our contribution average to predicted according its formation determined and strategy 2C). (Fig. embryo the of throughout function position a and as time molecules RNAP average transcribing the actively obtained of we number S1), Fig. Appendix, (SI tran- embryos multiple actively Methods and molecules (Materials RNAP gene single-molecule of the scribing number using RNAP the calibrated fluo- estimate of be to resulting FISH then number These could gene. the values the rescence to transcribing proportional actively molecules is puncta fluorescent the by driven gene reporter a of scripts eve of Eq. 2 Primary in stripe the forward Is put Window formation Formation. Time Stripe Transcriptional of the Driver to of contributions Control relative Binary their predict mRNA to cytoplasmic across us formation. regulated pattern the allows is quantity stripe of each the formation how measuring the Thus, pattern. to contributes egy in resulting analytically, by approximated be can it that average such time time its rate in synthesis significantly the vary embryo the throughout tion B section Appendix, (SI sfloecn ucawti niiulnce Fg 2B GFP (Fig. S1 nuclei to individual appear within formation fused puncta transcript nascent fluorescent protein of as sites coat result, recog- a MS2 are As 2A). provided loops (Fig. stem maternally These by (29). nized loops stem form transcribed, the enx sdorM2dt oeaiesailted in trends spatial examine to data MS2 our used next We sn h S ytm eqatfidec oeta regulatory potential each quantified we system, MS2 the Using .A ecie in described As ). eve tie2rpre,uigteM2sse 1,2,2) Tran- 28). 27, (18, system MS2 the using reporter, 2 stripe mRNA(x 2 |  ae rcs rdcin bu o ahrgltr strat- regulatory each how about predictions precise makes e −γ rmtrcnanrpaso N euneta,when that, sequence DNA a of repeats contain promoter t off rncitoa iewindow time transcriptional ( t eve −min{t (x :Weesi h etro the of center the in Whereas Methods): and Materials , ne hs supin,Eq. assumptions, these Under ). ntefutfl.W mgdtetasrpino an of transcription the imaged We fly. fruit the in t = ) off R entasrpinrate transcription mean (x ( x h nest fthese of intensity the S2, Fig. Appendix, SI ),t = ) {z }) .Scn,w oie hta ahposi- each at that posited we Second, ). hR − ots h ipemdlo pattern of model simple the test To ∆t | e (x R −γ {z (x γ , A eqatfidtasrpinof transcription quantified we 2, = t ( ) hsasmto srevised is assumption This )i. t } and t t −t on off on (x − B; ( x a osatars the across constant was ), 8 eve t )) on ± t n e.2) yaligning By 27). ref. and oi S2; Movie }  off R ssapymodulated sharply is , 4 × tie2ehne and enhancer 2 stripe (x × efis sdthe used first We 2. (x i fe h previ- the after min rcinactive fraction ) hwdastrong a showed ), sdsrbdin described as | p R 1 active 6molecules ∼16 (x a esolved be can {z IAppendix, SI , t (x and t ) on osnot does } ) (x Movie ) . and [2] SI e ou netmtso h iigo h perneand appearance the of timing the of estimates of on of experiments locus effects our potential per in for limit account detection to necessary the was it results, these for for only transcribing transcribing boundaries center stripe 3 the (Fig. in stripe the along time and space of function a embryos). as 11 over (C gene averaged Histone. (data the nascent to transcribing are RFP of Nuclei actively of molecules. Sites molecules fusion RNAP intensity (B) transcribing a whose GFP. actively through puncta of to visualized number fluorescent fused the green on protein as reports coat appear MS2 formation an by in transcript introduced bound loops are stem MS2 gene (A) system. MS2 the i.2. Fig. B A C esrn rncitoa yaisof dynamics transcriptional Measuring C and 0mn ent ht oderive to that, note We min. ∼10 D and 0mnadnce nthe on nuclei and min >30 NSLts Articles Latest PNAS eve ,wt nuclei with S3), Movie NPmolecules RNAP ∼4 ennme fRNAP of number Mean ) tie2frainusing formation 2 stripe eve tie2reporter 2 stripe | f12 of 3

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY A C E analog control of the mean transcription rate binary control of the transcriptional time window control of the fraction of active nuclei

10 m 10 m 10 m

0 5 10 15 20 25 0102030 transcriptional time window (min) B mean number of RNAP molecules D F 1 16 30 0.8 12 20 0.6 8 0.4

transcriptional 10 4 time window (min) 0.2 fraction of active nuclei

production rate (mRNA/min) 0 0 0 -4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4 distance from stripe center distance from stripe center distance from stripe center (% embryo length) (% embryo length) (% embryo length)

G 0.1 actual distribution of cytoplasmic mRNA

analog control of mean transcription rate

binary control of 0.05 transcriptional time window

stripe amplitude control of the fraction of active nuclei

analog + binary control 0 -4 -2 0 2 4 distance from stripe center (% embryo length)

Fig. 3. Regulatory strategies for pattern formation in eve stripe 2. (A–F) Time-averaged rate of mRNA production (A and B), transcriptional time window (C and D), and fraction of active nuclei as a function of position along the embryo (E and F). (G) Amplitude of the cytoplasmic mRNA distribution compared to the contributions to stripe formation of the analog control of the mean transcription rate, the binary control of the transcriptional time window, and the control of the fraction of active nuclei. The combined contribution from the analog and binary strategies is also shown. See SI Appendix, Fig. S5 for details of how depicted profiles were derived from raw data. A, C, and E show representative snapshots of an individual embryo 40 min into nuclear cycle 14; B, D, and F show average over 11 embryos; and error bars indicate bootstrap estimate of the SEM.

disappearance of fluorescent puncta. This procedure is outlined how much each of these strategies contributes to the cytoplasmic in detail in SI Appendix, section C, as well as in SI Appendix, Figs. mRNA pattern. To quantify the degree to which each regulatory S12 and S13. strategy contributes to the formation of eve stripe 2, we employed Finally, our analysis also revealed the magnitude of the mod- the model described in Eq. 2. ulation of the fraction of active nuclei along the stripe. Most Fig. 3G indicates the quantitative contribution of each reg- nuclei along the stripe were engaged in transcription. In the ulatory strategy (each term on the right-hand side of Eq. 2) stripe center, nearly 100% of nuclei transcribed at some point to the formation of this cytoplasmic pattern. The cytoplasmic during the nuclei cycle. This number reduced to about 80% at pattern of accumulated mRNA, corresponding to the left-hand the boundaries (Fig. 3 E and F and Movie S4). side of Eq. 2, was obtained by integrating from our live-imaging The analysis in Fig. 3 A–F reveals that each of the 3 regula- data (see SI Appendix, section B for details). Regulation of the tory strategies identified in Fig. 1 is at play in the embryo and fraction of active nuclei along the embryo (Fig. 3G, yellow) con- that they all have the potential to contribute to pattern forma- tributes negligibly to this mRNA pattern. In contrast, both the tion. However, these measurements alone cannot inform us on analog regulation of the mean rate (Fig. 3G, green) and the

4 of 12 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on December 28, 2019 Downloaded by guest on December 28, 2019 ytepouto h rcino ieseti h Nsaewith state (34–37) ON state the active in this spent in time rate of transcription fraction the the of product the by bursting. describes transcriptional this that in engage window loci time which over transcriptional period transcriptional the longer by a dictated periods and ON bursts transient short, the both correspond- (and, RNAP eve of rate at loading production) the mRNA in ingly, engage state ON the h rmtr hc sterlvn uniyfrestimating for quantity relevant the at is loading RNAP which of promoter, rate RNAP instantaneous the transcribing the actively how- on of not approach, number and molecules MS2 total the The of on time. dynamics reports contrast, ever, real the In on in inform bursting 39–47). directly transcriptional 31–33, can imaging (26, or live models embryos MS2-based fixed theoretical and transcriptional using dead of of cells snapshots measurements through from obtained inferred noise is of action release factor the in or (38). promoter pausing the promoter-proximal to from RNAP RNAP of recruitment the in variation instance, the For narrow r mechanisms. help molecular can possible regulation of to set identifying subject cycle, transcriptional is the parameter(s) in which step rate. molecular directly map single transcription necessarily a not to average does parameter the bursting each control While burst- to 3 the factors of transcription which identify (k to parameters parameters sufficient ing not bursting is mean these alone the trend However, of rate regulation. more controlled 3G, spatially (Fig. or to stripe subject one are the across that transcription implies of green) rate modula- mean observed the the of framework, tion this within Thus, position derivation). of function a embryo, as the vary along to allowed are parameters all where and switch- ON rates between promoter (26, with stochastically of approaches states switch OFF fixed-tissue model Promoters 4B): from 2-state (Fig. data minimal ing as a well imaging support live as 31–33), from 30), peri- evidence other 25, with and interspersed (18, This constant (18). activity a inactivity of of at “bursts” ods gene discrete the during onto rate molecules rapidly RNAP promoter multiple the with loading “burst-like,” the is at activity of initiation transcriptional the number RNAP to related the of been have in rate features troughs These molecules. and our RNAP 4A, peaks active Fig. in punctuated shown revealed as the Indeed, data within (18). loci stochastic highly individual is at pattern expression gene transcriptional the of that the demonstrated rate analyzed work Previous we nuclei. individual transcription, of of activity of control rate analog the mean behind the mechanism are To molecular process? or the underlying uncover mechanisms, same the molecular of manifestations distinct different they by driven on. strategies Turn Promoter trol of Modulation through Rate Bursting the by of Dictated Is Rate Transcription Mean to sufficient activity. from is mRNA transcriptional brown) cytoplasmic single-cell 3G, of the stripe (Fig. that the strategies recapitulate concluded 2 quantitatively thus these We of effect role. joint dominant binary the with pattern, playing overall control blue) the 3G, to (Fig. contributions window significant time make transcriptional the of control binary amr tal. et Lammers ol niaeta rncito atr lya cierl in role active an play factors transcription that indicate could ntebrtn oe,tema aeo rncito sgiven is transcription of rate mean the model, bursting the In yial,tei iomlclrmcaimo transcription of mechanism molecular vivo in the Typically, rncito rate transcription tie2tasrpinldnmc,w edt con for account to need we dynamics, transcriptional 2 stripe mean | R {z (x } ) on x , (see = k off k NPloading RNAP on and , eto A section Appendix, SI and | r rate {z (x r k sbigrgltdb h input the by regulated being is ) } ) off r h iayadaao con- analog and binary the Are eve r hsw n ht odescribe to that, find we Thus . nti oe,pooesin promoters model, this In . rmtrb suigthat assuming by promoter × | k rcino time of fraction on nO state ON in (x k o eal fthis of details for + ) on {z (x k ) off eve (x tie2 stripe ) } , k [3] on , NPmlclsi igecls(i.4 (Fig. cells single in loading molecules RNAP A RNAP of 49). rates 48, 4 the (Fig. (30, mean inferring traces for infer of method that computational ensembles methods across autocorrelation-based parameters or bursting dynam- 25) transcriptional (18, single-nucleus of ics relied analysis mainly manual have the organisms multicellular on in data such from ters k fluctuations; the fluorescent for background (F accounts chromatids. (In sister bursting. that transcriptional both punctum of fluorescent (E action a chromatid. combined within sister diffraction-limited a a switching within to promoter loci corresponding transcriptional each ( distinct approaches state. spot, 2 model promoter of Markov hidden composed the hidden infer are standard molecules to that used RNAP such be of cannot (Top), numbers gene observable the different on to correspond (C can promoter. tom) bursting single of model Two-state a (B) bursts. of in transcribe nuclei that reveal ments 4. Fig. fitrs tepooe tt)i n-ooefsin(com- fashion one-to-one a 4 in Fig. state) promoter pare variable hidden (the the to interest correspond of not does observ- are signal) our that MS2 However, states (the (50). able through observer the transitions to it accessible as directly not system a of dynamics the parameters. bursting the obtain D B A EF rmtrRNA promoter off

idnMro oes(Ms r ieyue ouncover to used widely are (HMMs) models Markov Hidden number of RNAP and , molecules 20 40 rncitoa usigin bursting Transcriptional 0 C, three-state model 02 040 30 20 10 r 0s rmtettlnme fatvl transcribing actively of number total the from Bottom) odt,apoce o xrcigbrtn parame- bursting extracting for approaches date, To . k k on off C, polymerase time (min) or Top 20s and h aehde aeo NPlaig(Bot- loading RNAP of rate hidden same The ) ro asaeotie rmetmto of estimation from obtained are bars error A, .Ised h bevbeMS2 observable the Instead, Bottom). r nascent mRNA 40s eve aeil n Methods and Materials igencesmeasure- Single-nucleus (A) 2. stripe effective two-statemodel C 60s C, NSLts Articles Latest PNAS fetv -tt oe of model 2-state Effective ) RNAP number of RNAP chromatids sister stu eddto needed thus is Top) k loading rate molecules on he-tt oe of model Three-state ) OFF loecn puncta Fluorescent D) ON 80s k RNAP counts state, different same promoter off time n e.27.) ref. and r 100s 5 μ | m f12 of 5

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY signal reflects the net effect of promoter switching over a period AC equal to the time that an RNAP molecule takes to transcribe 80 data the whole gene. Thus, instantaneous fluorescence does not just 60 inference depend on the current promoter state; it exhibits a dependence 40 on how active the promoter has been over a preceding window of 20 15 min time, which effectively constitutes a memory for recent promoter number of 0

states (24, 37, 51, 52). Classic HMM approaches cannot account RNAP molecules for this kind of system memory. B ON To model the process of transcription and extract the kinetic 20 min parameters of promoter switching, we augmented classic HMMs to account for memory (details about implementation of the method are given in SI Appendix, section D). Similar approaches state were recently introduced to study transcriptional dynamics in OFF 10 m 36 min cell culture and tissue samples (22–24, 53–57). We used sim- inferred promoter 10 20 30 40 time (min) ulated data to establish that cpHMM reliably extracts the D E kinetic parameters of transcriptional bursting from live-imaging 30 1.6 data (SI Appendix, section E), providing an ideal tool for dis- OFF ON secting the contributions from individual bursting parameters 20 1.2 to observed patterns of transcriptional activity across space ON and time. 0.8 Before applying our model to real-time transcriptional data, 10 0.4 we had to account for the rapid replication of the Drosophila ON OFF OFF melanogaster genome at the beginning of each nuclear cycle (58), 0 transition rate (1/min) 0

which leads to the presence of 2 distinct eve loci within each RNAP loading rate (1/min) -4 -2 0 2 4 -4 -2 0 2 4 fluorescent spot (Fig. 4D and Movie S5). The first evidence distance from stripe center (% embryo length) of resolved chromatids appears as early as 8 min into nuclear cycle 14 (SI Appendix, Fig. S24)—coincident with the average Fig. 5. Inferring bursting dynamics using a memory-adjusted hidden Markov model. (A and B) Representative experimental trace along with its onset time of transcription (SI Appendix, Fig. S4B). Moreover, best fit (A) and its most likely corresponding promoter state trajectory (B). our analysis indicates that replication of the relevant portion of (C) Instantaneous visualization of promoter state in individual cells through- the genome likely occurs in all eve-expressing nuclei by no later out development through the false coloring of nuclei by promoter state than 10 min following mitosis (SI Appendix, Fig. S24). Thus, we (colors as in B). (D) The rate of initiation for each transcriptional state is not conclude that the vast majority of our data feature 2 distinct significantly modulated along the embryo. (E) Our cpHMM reveals that the eve loci within each diffraction-limited transcription spot. More- transition rate between the OFF and ON states (equivalent to the burst fre- over, while the distance between sister loci varies over time (e.g., quency) is up-regulated in the stripe center. (In A, error bars are obtained Fig. 4D), they nonetheless stay in relatively close proximity to from estimation of background fluorescent fluctuations, as described in ensure their proper segregation from each other at the next mito- Materials and Methods and ref. 27; in D and E, error bars indicate the mag- nitude of the difference between the first and third quartiles of cpHMM sis (59) such that the fluorescent intensity signals extracted from inference results for bootstrap samples of experimental data taken across our data reflect the integral over both loci (SI Appendix, Fig. S2). 11 embryos; see Materials and Methods for details.) As a result, if we assume that each locus can be well represented by a 2-state model (OFF/ON) of transcriptional bursting, then an effective 3-state model (OFF/OFF + OFF/ON + OFF/ON + To infer time-averaged bursting parameter values, we grouped ON/ON) is needed to capture eve dynamics (Fig. 4E). Thus, we traces by position along the anterior–posterior axis. The rate elected to employ such a 3-state model in our analysis. Due to of RNAP loading, r, remained constant throughout the stripe conflicting evidence from previous studies (26, 32, 60), we made (Fig. 5D), suggesting that none of the transcription factors reg- no prior assumptions about the nature or degree of cooperativity ulating eve stripe 2 act directly on the rapid series of molecular between sister chromatids either in transitions between activity steps involved in the initiation of transcription by RNAP. Sim- states or in the rates of initiation in each state (see SI Appendix, ilarly, we noted no significant spatial modulation of the rate of section E for details). While these assumptions increased the switching out of the ON state, koff (Fig. 5E). In contrast, the rate complexity of our model, we believed that a conservative of switching into the ON state (also known as burst frequency), approach that left the model free to infer the presence or absence kon, was strongly up-regulated in the stripe center (Fig. 5E). of sister interactions was warranted, given our ignorance regard- These observations suggested that, to control the mean rate of ing the nature and strength of interactions between adjacent transcription, transcription factors act primarily on the rate of gene loci. For ease of exposition, we present our main results promoter turning on, consistent with previous results both in in the context of an effective 2-state model, in which, as detailed embryos (25, 30, 33) and in single cells (41, 43, 44, 46). This in SI Appendix, section A, the system is considered to be in the regulatory modality increases the fraction of time that loci near ON state as long as either chromatid is bursting (Fig. 4F). Note the stripe center spend in the ON state (SI Appendix, Fig. S7 that none of our conclusions below are affected by this choice of and ref. 26). an effective model as shown in SI Appendix, section G, where we present full results for the 3-state model. Binary Control of the Transcriptional Time Window Is Independent A typical experimental trace for a nucleus in the core of the of Transcriptional Bursting. Having determined that the analog stripe is shown in Fig. 5A, along with its best fit, which corre- control of the mean transcriptional rate is realized by the mod- sponds to the cpHMM-inferred promoter trajectory in Fig. 5B. ulation of the burst frequency, kon, we next sought to uncover Our ability to infer the instantaneous promoter state in indi- the molecular mechanism by which the binary regulation of the vidual nuclei throughout development is further illustrated in transcriptional time window is implemented. In one possible Fig. 5C and Movie S6. These data revealed that, as devel- scenario, the onset of transcriptional quiescence at the end of opment progresses and the stripe sharpens, the eve promoter the transcriptional time window would reflect a fundamental continuously fluctuates between the ON and OFF states on a change to the molecular character of the transcriptional locus time scale of ∼1 to 2 min. such that the bursting framework no longer applies. For instance,

6 of 12 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on December 28, 2019 Downloaded by guest on December 28, 2019 unhn neatos(2,amcaimta ol manifest would capac- in that short-range the decrease mechanism via a disrupt a as bursts (62), could transcription interactions initiate flanks quenching to stripe activators for the increasing ity in that possible levels is it repressor example, reducing For bursts. progressively transcriptional by (k achieved frequency be the could quiescence transcrip- tional Specifically, the bursting. from transcriptional distinct invok- mechanistically driving is without processes that explained state be silenced extra could an window ing time the then time, in amr tal. et Lammers see boot- data; for results experimental details.) inference of cpHMM of samples quartiles position strapped third and and first time the (E between in of state function SEM; ON frac- a the the (In as the in quiescence. (F OFF of is scriptional stripe to analysis promoter ON the our the from along for rate when transition (B) loading the RNAP regions of 5 (C nuclei rate into quiescent stripe of (A, tion the and of state silent Division transcriptionally (A, long-lived onset: alternative, ii quiescence an promoter into explaining sition hypotheses Two (A) 6. Fig. the at factors bursting transcription activator-mediated general the further 6 at abolish (Fig. of activators thus or of and binding promoter enhancer the 2 block could stripe nucleosomes that enhancer changes or chromatin (61), promoter local repositioning the by alter landscape could factors transcription repressing B A h ouaino n rmr usigprmtr vrtm.(B–F time. over parameters bursting more or one of modulation the ) RNAP rate molecules (ii) temporalcontrolofburstingkinetics 6- 22 -2 -4 -6 netgtn h oeua hrce ftasrpinlquiescence. transcriptional of character molecular the Investigating A, (i) transitiontoasilentstate k k r distance fromstripecenter OFF ON .Atraiey ftertso rmtrsicigvary switching promoter of rates the if Alternatively, i). ON (% embryolength) ro asidct h antd ftedifference the of magnitude the indicate bars error D–F, k silence time k 046 on on .Ga hddrgo niae h ne ftran- of onset the indicates region shaded Gray ). silenced ,itniy(r intensity ), vrtime. over ,tetasto aefo F oO ( ON to OFF from rate transition the ), ro asidct otta siaeof estimate bootstrap indicate bars error C, ? F E C D ,ado uain(1/k duration and/or ), -1 koff (min ) -1

0.3 0.6 0.9 1.2 1.5 k (min ) fraction of quiescent nuclei rate of RNAP loading (1/min) 0.4 0.8 1.2 1.6 on 0.2 0.4 0.6 0.8 10 15 20 25 30 0 0 5 0 2 0 1 02 040 30 20 10 02 040 30 20 10 aeil n Methods and Materials 02 040 30 20 10 02 040 30 20 10 time (min) time (min) time (min) time (min) ON OFF ON i tran- a ) off ,the D), ,and ), OFF of ) ON for ) otoldb itntmlclrpoess hndsic forms distinct then processes, molecular distinct are window by time and controlled transcriptional the Bicoid and Kr activators, bursting and 2 transcriptional Giant repressors, of 2 action and Hunchback, combined Window. the Time by Burst- Transcriptional established for the Logic and Regulatory ing Distinct Reveals Analysis distinct Input–Output from arise may rate transcription processes. analog mean molecular the the driving of bursting transcriptional control the and window 6 time (Fig. model bursting the A, within might captured quiescence not transcriptional processes into involve entry that indicated flanks unlikely. changing is detect are that to dynamics concluded quickly bursting we too where min), scenario 15 a to on possible, (5 while unfold scales H) time section tran- slower input Appendix, significantly the (SI in trends burst- themselves shifts temporal the as factors other and the scription 6) scale both (Fig. time inference that our Given same by min). detected the 3 to on (1 occur itself ing to our by need in captured would be discussed changes to with as rapidly However, associated too occur model. parameters quiescence bursting of conceivable onset in is the It changes parameters. temporal switching mod- that temporal promoter the the by counter driven of runs is ulation good, quiescence for that off hypothesis turn the nuclei to neighboring or in loci constant as remaining even bursting at transcriptional of increasing rate the with and transcriptional of activity establishment transcriptional 6D)—a the quiescence. against (Fig. overall go stripe increase therefore the would of would we regions 6C), that inner Fig. trend some in in in curves increased decrease yellow ally min corresponding and 40 (blue no by quiescent cycle detected were nuclear center the stripe the into of posterior and rior be in decrease could a that flanks view stripe the the 6 (Fig. in parameters suggest bursting A, of quiescence to modulation temporal part, seemed the by in 6D) driven least Fig. the at in of region that, shaded coincidence (gray the quiescence Moreover, in dynamics. decrease transcription influence factors 2 transcription which through pathway 5 (Fig. loading 6 RNAP (Fig. of slightly state, decreased rate ON the the both in stripe, when the along exhibited curves) positions yellow most and in green increase 6D, loci significant (Fig. while a center region), of stripe shaded fraction the gray the 6D, in and (Fig. progressed decreased nuclei development active black as 6D, curves) (Fig. boundaries red stripe and posterior and on, anterior 6 turn the (Fig. both promoter time of in using rate significantly ied the data that live-imaging revealed our (see inference on window of sliding inference periods a performing to discrete in by method over dependence time cpHMM time parameters our this promoter-bursting extended for obtain probe we manner determine To bursting, indi- a of 6C). to transcriptional quiescence in (Fig. into sought entry time nuclei of we over vidual dynamics regions the region, varied explain 5 each could dynamics that the bursting For into the 6B. stripe whether Fig. the in divided shown we framework, bursting h otaitr rnsosre ntesrp etrand center stripe the in observed trends contradictory The h iegn ucmsosre ntecnrlsrp regions, stripe central the in observed outcomes divergent The ante- directly regions the in nuclei of 50% and 70% Although results inference time-averaged our confirmed findings These the within explained be can quiescence whether determine To .Hwvr te rnsi u aawr o ossetwith consistent not were data our in trends other However, ii). ,tu ugsigta iaycnrlo h transcriptional the of control binary that suggesting thus i), D and k on eve niaigthat indicating E) nfln uliwt h ne ftranscriptional of onset the with nuclei flank in oiwti h nae ouaino nuclei of population engaged the within loci r eto D section Appendix, SI k n h aeo rmtrtr off, turn promoter of rate the and , on E k ute,wierltvl osatat constant relatively while Further, . and on rvstasrpinlquiescence. transcriptional drives .Specifically, D). F ). these , I section Appendix, SI k on NSLts Articles Latest PNAS a h rmr kinetic primary the was k eve on pe 1,1,6) If 63). 17, (16, uppel ¨ nfact, In . tie2i mainly is 2 stripe o eal) Our details). for k on erae in decreased eve k k on | on stripe f12 of 7 var- , actu- k off ,

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-3 F B relative log-likelihood (10 ) time (min) 40 30 20 40 30 20 10 10 15 0 5 time (min) distance fromstripecenter(%embryolength) 40 35 30 25 20 15 10 Gt Kr Kr β 1234 4 [Kr], number oftranscriptionfactors 505 -5 [4] Hb Gt Gt Kr Kr 505 -5 etoe oespeetdhr ssonin shown as here improve- presented H significant section models to no over activators provided transcrip- ment presumed repressors—also each the as of allowing function role instance, functional factor—for the tion on silencing. pre- constraints for no responsible model’s Relaxing processes have molecular the concentrations the on activator over influence that impact of suggesting no addition power, had dictive further Bicoid The recapitu- and/or trends. respectively, Hunchback Giant flanks, observed stripe repressors experimentally posterior lated the and of anterior levels the increasing Kr which and in model ple tt eeldta osnl rncito atrcnexplain can 7 factor (Fig. transcription single dynamics no quiescence that revealed state ikdars nHnhakcnetaint nosre iein rise observed an to concentration Hunchback spa- in rise the a capture linked 7 fully (Fig. to dynamics necessary bursting tiotemporal also profile; were bursting observed levels the ON Hunchback recapitulate the not in could alone nuclei levels of fraction 7 the (Fig. state and levels factor transcription OFF the versus ON bursting the in are state, analogous nuclei relative transcrip- the an that determine controlling that used likelihood factors logic the We inferring regulatory by 16). bursting the tional (5, investigate roles to repressing Kr model and play requiring roles to activating factor, play Giant transcription to each Hunchback knowl- of and prior Bicoid function used the we about coefficients, edge these regulatory estimating factor’s In transcription function. the of repressing) or (activating coefficients the where u nlsso h rcino ulii h quiescent the in nuclei of fraction the of analysis Our enx undoratnint h eainhpbetween relationship the to attention our turned next We P Bcd pe rv h ne ftasrpinlqisec in quiescence transcriptional of onset the drive uppel ¨ ON Hb 0.7 0.1 0.3 0.5

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in that engage promoters discovered which alone gene We over of is time green). stripe transcription sharp 3G, a of (Fig. of formation rate expression the mean for account the to of insufficient modulation in the located that enhancers the other dissect endogenous of free to the influences element us confounding regulatory the allowed the well-characterized from that a by of approach driven logic an regulatory reporter enhancer, MS2 gene 2 an live- stripe cytoplasmic single-cell the of to a model measurements at this of imaging applied strategies We formation (2). regulatory the pattern expression between dictates interplay level single-cell the how dicts among whether, mechanism strategies. or apparent control readily distinct expression most multiple gene the simply of is patterns it factors instead, of transcription formation of the concentrations dominant of drive input the rate which is the by production) of modality mRNA modulation thereby this Yet (and whether 68). transcription 33, unclear the 30, remained tuning 26, has by 25, it (18, embryo bursting the transcriptional across of frequency modulated tran- mainly of rate is average the scription that play. revealed at have implica- processes studies molecular recent distinct underlying Several with the of each nature 1), the for (Fig. tions mRNA cytoplasmic of pat- differentiated terns spatially generating of capable level single-cell pattern spatiotemporal a to mRNA. rise cytoplasmic gives of activity level transcriptional single-nucleus of critical regulation the a the at how on cascade: light this shed in to link in modeling imaging theoretical live utilized with we conjunction Here, of dogma. infor- central of prediction the flow along the The mation facilitate quantitative that plan. a mechanisms the requires body of outcomes understanding adult developmental culminating of the genes, cascade of this interacting specification of the through in propagates layers gradients complex protein increasingly 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input–output Our Drosophila eeomn,ifrainecddi handful a in encoded information development, eve ou.W demonstrated—quantitatively— We locus. eve ssonin shown As eve. tie2ehne ntecontext the in enhancer 2 stripe C B and and Fig. Appendix, SI n htthe that and D) F ). eve locus eve nlsso nvv ellrpoessa h igemlcl level. biophysical single-molecule broader the the at processes for cellular useful novel vivo the be in in of will of analysis protein method state envi- our into we that the Thus, translated sion (76–79). infer For assays being translation 48). to vivo is in (25, used single-molecule organism mRNA be any as in could ribosome systems method approaches PP7 using the or tagged underlying example, MS2 is the the that infer as gene to such any useful of prove kinetics should regulatory and active system 2 the stripe in spend nuclei that time of state. transcriptional fraction the increase to across factors transcription our input eve the 33), by 30, regulated (k 26, parameter frequency ing burst (25, the measure- patterns that previous expression revealed with results gene agreement various parameters In of 5). promoter-switching used ments (Fig. We average stripe traces. the infer instantaneous MS2 across to the from devel- loci inferring cpHMM we gene of this stripe, individual capable of the is mean state across that activity the cpHMM regulated how a is uncover oped transcription with to conjunction of Specifically, in rate data. tools vari- live-imaging a computational utilized and our transcription we theoretical mean mechanism, of the molecular ety of common control a share analog rate the and window time (69). state steady of outside a occurs is that development embrace that process and acknowledges 31–33) that (26, description on embryos dynamical fixed focused a of that snapshots of studies study static pre- activity the by steady-state, transcriptional forward widespread put single-cell development vious the in will a formation beyond it pattern toward of go development, progress picture to in make formation to necessary is pattern be field of the picture recent if predictive these strategy that, Together, expression. a suggest gene findings as of dif- engagement patterns the generate transcriptional leverages to of actively duration embryo the ferential that quiescence, demonstrating transcriptional thus of of onset interplay shown case the the have the We of in regulation. aspect that, transcriptional another and previous timing yet these between complements exploring work by Our fol- observations 75). activation (74, transcriptional regulating mitosis of in probability role lowing key and a timing plays the Zelda both of factor architec- pioneer patterns the regulatory observed that cated driving of for kinds responsible hunchback be the could on time that ture readout constraints limited strict early the in places cycles that nuclear short demonstrated by imposed has one reg- study example, transcriptional For recent outcomes. of developmental specifying component the in temporal ulation regarding the biology of developmental importance quantitative in discourse any of formation. duration element pattern differential of necessary description a the quantitative comprises Thus activity formation. transcriptional pattern of drive fac- to transcription tors by regulated actively is the logic engaged/quiescent that—in this demonstrated have of we course case 72), that the 28, over appreciated (27, time widely of development windows of is discrete for it expressed While are genes (71). a data and experimental demanding predictions the theoretical the beyond and our going formation between by agreement pattern quantitative is, of that description numeracy, qualitative the biological pos- of by made duration was only differential formation sible the pattern in of window role time 70). key transcriptional 67, the (33, of phenomena discovery insights biological Our extract complex to of workings models the quantitative into simple using of utility the motnl,orcHMagrtmi o iie othe to limited not is algorithm cpHMM our Importantly, transcriptional the of control binary this whether determine To exciting and diverse increasingly an to contributes work Our illustrates that work of body growing a to contribute we Here, tie2 hsices in increase This 2. stripe eeepeso 7) te eetwr a indi- has work recent Other (73). expression gene eve tie2rpre—hsbnr transcriptionally binary reporter—this 2 stripe eve tie2 rncito atr regulate factors transcription 2, stripe k on ntesrp etrfunctions center stripe the in NSLts Articles Latest PNAS Drosophila t off on costestripe, the across , stemi burst- main the is ) development | f12 of 9 eve

DEVELOPMENTAL BIOPHYSICS AND BIOLOGY COMPUTATIONAL BIOLOGY Having identified kon as the primary kinetic mode by which that the embryo does not encounter in the wild-type setting. transcription factors modulate the mean rate of expression For instance, our finding that repressors alone drive the onset across eve stripe 2, we next sought to probe the relationship of transcriptional quiescence predicts that this onset should be between bursting and the transcriptional time window (Fig. 6A). unaltered in mutated eve stripe 2 enhancers where some or We adapted our cpHMM to go beyond time-independent mod- all Hunchback binding sites have been disrupted. In this sce- els of promoter switching to infer the regulation of these rates nario, transcriptional activity, initially arising due to permissive across both space and time. We observed striking temporal levels of Bicoid, would shut off in precisely the same manner as trends indicating that the burst frequency responds dynamically observed for the full enhancer (compare Fig. 7 D, Upper Right to time-varying transcription factor inputs. However, we noted a to Fig. 7 D, Lower Left). In the absence of Hunchback activa- significant disconnect between temporal trends in the burst fre- tion, the model also predicts reduced levels of transcriptional quency and the onset of transcriptional quiescence. In particular, bursting, particularly late in nuclear cycle 14 (compare Fig. 7 F, kon either increased or remained constant near the stripe cen- Upper Right to Fig. 7 F, Lower Left). Similarly, our model could ter even as a significant fraction of eve nuclei transitioned into be used to predict the expected stripe profile in mutant embryos, quiescence (Fig. 6 C and D). We reasoned that the onset of where the expression of one or more gap genes has been altered transcriptional quiescence is likely not the result of a progres- or abolished. We note, however, that the interconnected nature sive reduction in burst frequency, amplitude, or duration and of the gap gene network (9) means that it would be necessary is instead driven by molecular processes that are distinct from to reimage all 3 gap genes that regulate eve stripe 2 to gener- those that regulate transcriptional bursting, such as a repressor- ate data such as shown in Fig. 7C, since any change to one will induced shift in nucleosome position that prevents activating affect the expression patterns of all. Thus, additional binding- transcription factors from binding to the stripe 2 enhancer. site mutation studies similar to the one described above likely To test this hypothesis, we utilized a logistic regression frame- represent the most direct path to testing our model’s predic- work and time-resolved data for the primary regulators of eve tions. Taken together, we anticipate that the approaches outlined stripe 2 to query the regulatory logic exhibited by the time in this work will serve as a tool both for extracting additional window and bursting, respectively (SI Appendix, section H). In insights from experimental data and for motivating additional this context, the logistic regressions served as a robust statis- experiments aimed at answering meaningful questions about the tical tool for drawing inferences from existing data that were mechanistic underpinnings of gene regulation. not obvious (or verifiable) by simple visual inspection. Consis- We also observe that certain aspects of the system remain tent with our time-resolved cpHMM results, the 2 regulatory beyond the scope of our model. Most notably, while loci engaged strategies responded to transcription factor concentrations in dif- in transcriptional bursting appear to continuously sense changes ferent ways. On the one hand, increasing levels of Giant and in transcription factor concentrations, it remains an open ques- Kruppel¨ were sufficient to explain the onset of transcriptional tion whether loci continue to read out transcription factor con- quiescence in the stripe flanks (Fig. 7 A and D). This observation centrations following the onset of transcriptional quiescence. points to a model in which repressor levels act unilaterally— While the transition appears irreversible in our data, it is possi- without respect to coincident levels of activator proteins—to shut ble that quiescence is, in fact, reversible but simply not observed off transcription at loci in an (at least effectively) irreversible because repressor levels increase over time in our region of fashion. Conversely, the joint action of Giant, Kruppel,¨ and interest. The temporally resolved manipulation of repressor con- Hunchback was necessary to recapitulate the observed pattern centration through, for example, optogenetics (81) could make it of transcriptional bursting (Fig. 7 B and F). possible to deplete repressors from the nucleus after transcrip- This difference in the regulatory logic observed for the 2 tional quiescence to determine whether this quiescent state is strategies dissected in this work suggests that control of the reversible. transcriptional time window and the modulation of the aver- To further test these and other hypotheses, it will be critical age transcription rate arise from 2 distinct, orthogonal molec- to move beyond spatiotemporal averages for transcription factor ular mechanisms. It is also notable that our model finds that inputs (Fig. 7C) and, instead, use live single-nucleus measure- Hunchback activation is necessary to fully explain the observed ments to directly correlate input transcription factor concentra- pattern of transcriptional bursting in eve stripe 2. A recent study tion dynamics with the corresponding transcriptional activity at has suggested that Hunchback actually functions as a repressor of the single-cell level (82). Experimentally, we recently demon- eve stripe 2 and that indirect activation occurs via counter repres- strated the simultaneous measurement of inputs and outputs in sion of Hunchback by the maternal factor Caudal (80). While single nuclei of a living fly embryo using genetically encoded we cannot rule out the possibility that Hunchback acts indirectly, LlamaTags (83). We believe that using this technique, in con- the strong link between rising Hunchback levels and the increase junction with the theoretical methods presented here, to query in eve 2 activity in the stripe center is most consistent with the effects of targeted disruptions to transcription factor bind- Hunchback playing a traditional activating role. Additional work ing domains on regulatory enhancers will constitute a powerful will be necessary to determine whether this correlation between assay for querying transcription factor function at the molecular rising Hunchback levels and increased stripe activity can be rec- level. Thus, there are clear experimental and theoretical paths onciled with the counter-repression hypothesis proposed in ref. to uncovering the detailed quantitative mechanisms behind the 80. Finally, we note that the striking absence of a direct func- molecular control of transcriptional bursting and quiescence in tional role for Bicoid in the regulation of either phenomenon development. Such a quantitative description is a necessary step suggests that, while Bicoid is almost certainly necessary for the toward a predictive understanding of developmental decision expression of eve stripe 2 (16), it does not play a direct role in making that makes it possible to calculate developmental out- dictating the magnitude or duration of eve stripe 2 transcription. comes from knowledge of the nature of the transcription factor In this interpretation, Bicoid functions like a general transcrip- interactions within gene regulatory networks. tion factor, facilitating the transcription of eve 2 without directly conferring spatiotemporal information. Materials and Methods In addition to gleaning valuable insights into the mecha- Reporter Construct. This work employed the same eve stripe 2 reporter con- nisms driving the regulation of transcription of the eve stripe struct developed by ref. 18. This construct contains the even-skipped (eve) 2 enhancer, our logistic regression framework makes quantita- stripe 2 enhancer and promoter region (spanning −1.7 kbp to +50 bp) tive and falsifiable predictions about the regulation of this stripe upstream of the yellow reporter gene. Twenty-four repeats of the MS2 stem for combinations of input transcription factor concentrations loop sequence were incorporated into the 50 end of the reporter gene.

10 of 12 | www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Lammers et al. Downloaded by guest on December 28, 2019 Downloaded by guest on December 28, 2019 0 .Sgl .RvhSda .Shodr .Unrtl,U al rdcigexpression Predicting Gaul, U. Unnerstall, U. Schroeder, M. Raveh-Sadka, T. Segal, E. 10. 6 .Sal .Bar .Lvn,Rglto fee-kpe tie2i h Drosophila the in 2 stripe even-skipped of Regulation Levine, M. Blair, A. Small, S. 16. Drosophila Park of J. control 15. predictable Quantitatively Stern, L. D. Ilsley, R. G. logic Crocker, cis-regulatory J. The Barolo, 14. S. Cohen, A. B. Ramos, I. A. White, A. M. Parker, S. D. 13. perturbation- Quantitative Arnosti, N. D. Taylor, R. B. Pushel, I. Dresch, M. J. Sayal, R. 11. 0 .Z uwg au .Ktlr .P ht,M rimn osqecso eukaryotic of Consequences Kreitman, M. White, P. K. Kittler, R. Manu, Ludwig, Z. M. 20. Surkova employs S. enhancer 19. 2 stripe eve The Small, S. Levine, M. Barolo, S. Arnosti, N. D. 17. Fakhouri D. W. 12. 3 .Molina N. 23. of decoding Optimal Gregor, Suter T. M. Wieschaus, D. F. E. 22. Bialek, W. Tkacik, G. Petkova, D. M. 21. Bothma P. J. 18. 1,2,3) hsatraieso emnainapoc a on to found was approach segmentation Gaussians spot of alternative Difference In this on of (84). based 32), identification algorithm algorithm 27, FastRandomForest the previous (18, the a to using with modifications FIJI comparison Seg- with for Weka Trainable plugin 27 the using mentation ref. segmented were which in spots, transcriptional protocol the on cleavage nuclear Analysis. into Image min 40 of windows. spectral minimum 669-nm a to 14. for 566- cycle a and imaged using 546-nm were were to respectively, Detec- 498- Specimens Histone-RFP nm, Hybrid the separate and using 556 2 MCP-GFP (HyD) with and tors detected col- The 488 was were Fluorescence s. of Laser. stacks Light 21 wavelengths White Image of laser collected. resolution with was time excited nm 1.2 a stack 500 of a at by time point, time lected separated dwell each images pixel At repetitions. 21 a 3 of and over accumulated nm of was 212 frame output each of the size at pixel (measured a specimen micro- confocal the scanning a 10× on Data laser a power between coverslip. SP8 laser 27 Leica a Average a oil scope. and using halocarbon performed Starstedt) was film; in collection gene. (Lumox mounted reporter membrane and the semipermeable bearing collected males to were MCP-GFP crossed His-RFP; Embryos were yw; of protein) virgins coat female MS2 short, (MCP, In 18. ref. in described Collection. cedures Data and Preparation Sample amr tal. et Lammers .J Jaeger J. 9. enhancers multiple permits repression Short-range Levine, M. Szymanski, P. Gray, S. activa- 8. bhlh with the interactions cooperative by and pattern affinities body Binding Levine, Drosophila M. of Jiang, Control J. Lawrence, 7. A. P. Johnston, pair- P. a of Struhl, regulation Transcriptional G. Levine, M. 6. Warrior, R. Hoey, T. Kraut, R. Small, S. 5. N in C. parasegments synthetic Driever, of A W. Borders doug: 4. Struhl, of G. Macdonald, appeasement P. The Johnston, P. DePace, Lawrence, H. A. A. P. Estrada, 3. J. Vincent, J. B. Davidson, 2. H. E. Peter, S. 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